Statistical Methods for Infectious Disease Prevention Studies with Varying Exposure Risk

Abstract

In infectious disease prevention studies, participants' risk of infection can vary greatly due to differences in their underlying risk of being exposed to infection, which can complicate assessments of the association between an intervention and the risk of infection. For many infectious diseases with seasonal or otherwise temporal epidemic patterns, the risk of exposure to infection can vary dramatically with changes in the scale of the epidemic in the community, causing infection risk to be dependent on calendar time. In Chapters 2 and 3 of this dissertation, we provide statistical methods to account for differences in the risk of exposure to infection in time-to-event studies due to temporal epidemic patterns. Additionally, for diseases such as human immunodeficiency virus (HIV) and other sexually transmitted infections (STIs), variations in behavior can drive differences in participants' risk of exposure to infection but are difficult to capture with covariates. In Chapter 4, we identify settings in which unobservable heterogeneity in risk of exposure can cause bias in efficacy estimates and, in some cases, false effect modification, with a particular focus on nested case-control studies.